Abstract
Consumers of resources in realistic applications (e.g., web, multimedia) typically derive diminishing-return utilities from the amount of resource they receive. A resource provider who is deriving an equal amount of revenue from each satisfied user (e.g., by online advertising), can maximize the number of users by identifying a satisfaction threshold for each user, i.e., the minimal amount of resource the user requires in order to use the service (rather than drop out). A straightforward approach is to ask users to submit their minimal demands (direct revelation). Unfortunately, self-interested users may try to manipulate the system by submitting untruthful requirements. We propose an incentive-compatible mechanism for maximizing revenue in a resource allocation system where users are ex-ante symmetric (same amount of revenue for any satisfied user) and have diminishing-return utility functions. Users are encouraged by the mechanism to submit their true requirements and the system aims to satisfy as many users as possible. Unlike previous solutions, our mechanism does not require monetary payments from users or downgrading of service. Our mechanism satisfies the number of users within a constant factor of the optimum. Our empirical evaluation demonstrates that in practice, our mechanism can be significantly closer to the optimum than implied by the worst-case analysis. Our mechanism can be generalized to settings when revenue from each user can differ. Also, under some assumptions and adjustments, our mechanism can be used to allocate resource periodically over time.
Original language | American English |
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Pages | 1097-1098 |
Number of pages | 2 |
State | Published - 1 Jan 2011 |
Externally published | Yes |
Event | 10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011 - Taipei, Taiwan, Province of China Duration: 2 May 2011 → 6 May 2011 |
Conference
Conference | 10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011 |
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Country/Territory | Taiwan, Province of China |
City | Taipei |
Period | 2/05/11 → 6/05/11 |
Keywords
- Auction and mechanism design
All Science Journal Classification (ASJC) codes
- Artificial Intelligence